Chapter-2 Data Handling using Pandas - II — Online MCQ Test
INFORMATICS PRACTICES · Grade 12 · CBSE(NCERT)
Practice Chapter-2 Data Handling using Pandas - II with a free chapter-wise online MCQ test.
This chapter covers: DataFrame operations - missing values - NaN - descriptive statistics - mean - median - mode - sorting - CSV import - CSV export.
AI-generated questions from basic to board-exam level, with instant results and explanations.
Chapter-2 Data Handling using Pandas - II — Important Questions & Answers
What does NaN stand for in pandas?
- A. Not a Number
- B. Not a Name
- C. Null and Null
- D. No Available Number
Answer: A. Not a Number
NaN is a standard representation in pandas and NumPy for missing or undefined numerical values.
NaN is a standard representation in pandas and NumPy for missing or undefined numerical values.
Which method is used to check for missing values in a pandas DataFrame?
- A. isnull()
- B. ismissing()
- C. findnull()
- D. checknull()
Answer: A. isnull()
The isnull() method returns a boolean DataFrame indicating which values are NaN or missing.
The isnull() method returns a boolean DataFrame indicating which values are NaN or missing.
If a DataFrame has missing values represented as NaN, which method fills them with the mean value?
- A. df.fill_na()
- B. df.fillna(df.mean())
- C. df.replace_missing()
- D. df.impute_mean()
Answer: B. df.fillna(df.mean())
The fillna() method combined with mean() fills NaN values with the mean of the respective column.
The fillna() method combined with mean() fills NaN values with the mean of the respective column.
Consider a student marks DataFrame. If you need to identify and count all missing values across the entire DataFrame, which method would you use?
- A. df.count_na()
- B. df.isnull().sum().sum()
- C. df.missing_values()
- D. df.count_missing()
Answer: B. df.isnull().sum().sum()
df.isnull() returns boolean values, .sum() counts NaN per column, and another .sum() totals across all columns.
df.isnull() returns boolean values, .sum() counts NaN per column, and another .sum() totals across all columns.
Consider a scenario where you need to keep track of which values were originally missing after imputation. What would be the best approach?
- A. Don't impute, just use dropna()
- B. Create a separate boolean column marking NaN locations before imputation
- C. Impute and then check describe() for changes
- D. Use fillna() with a unique marker value instead of statistics
Answer: B. Create a separate boolean column marking NaN locations before imputation
Creating a mask column before imputation preserves information about original missing values for auditing and analysis.
Creating a mask column before imputation preserves information about original missing values for auditing and analysis.